Magnifying systems such as microscopic imaging systems are commonly used for conducting research, quantitative characterization and screening in various applications, such as semi-conductors fabrication, pharmaceutical research, biomedical and biotechnology laboratories, aerospace and automotive parts manufacturing. The measurements of attributes characterizing the elements present in microscopic images, finds applications in materials science and in pharmaceutical and biotechnological research. In order to compute accurately and precisely such attributes that accurately and truly reflect the spatial properties of the elements being imaged, a microscopic imaging system must be calibrated beforehand. This process establishes calibration parameters by measuring a reference object image having known attributes such as its physical dimension and shape. The nature of the calibration parameters can be quite complex, as their purpose is to compensate for all types of deformations and inhomogeneities induced by the entire imaging system, including non-linear effects due to all the optical/photoelectronic sub-components.
A typical calibration method requires the user to measure on a computer screen, using interactive image processing software tools, the distances between various elements of the image of a reference calibration pattern, as well as to compute other image characteristics using these same tools. This process is repeated for each magnification of the imaging system and requires each time from the user new adjustments of the microscope, photoelectronic sensor and the digital acquisition parameters. The calibration process generally requires the user to precisely identify the position of sharp edges, a task that is inherently subjective and that provides highly variable results between individuals, and also from the same individual at different times.
The accuracy and precision of the measurements of a microscopic imaging system are directly affected by the variability of the calibration process. Attempts to reduce this variability and uncertainties include restricting the performance of the calibration steps to a few trained users that thoroughly understand the details of the calibration methods, and averaging calibration results obtained at different times in order to reduce variability. The complexity of these procedures is a factor that prevents a broader adoption of computerized magnifying measurement systems, as they are still considered as complex and sophisticated tools that require a dedicated technical expertise to be properly operated. Thus better calibration and measurement methods are needed, as well as improved ways to characterize and validate the calibration results.